Search results for: transformer neural networks

Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: P. Ramesh, Aby Joseph, Arya G. Lal, U. S. Aji

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Integration of distributed green renewable energy sources in addition with battery energy storage is an inevitable requirement in a smart grid environment. To achieve this, an Intelligent Green Energy Management System (i-GEMS) needs to be incorporated to ensure coordinated operation between supply and load demand based on the hierarchy of Renewable Energy Sources (RES), battery energy storage and distribution grid. A bi-directional energy harvester is an integral component facilitating Intelligent Green Energy Management System (i-GEMS) and it is required to meet the technical challenges mentioned as follows: (1) capability for bi-directional mode of operation (buck/boost) (2) reduction of circuit parasitic to suppress voltage spikes (3) converter startup problem (4) high frequency magnetics (5) higher power density (6) mode transition issues during battery charging and discharging. This paper is focused to address the above mentioned issues and targeted to design, develop and implement a bi-directional energy harvester with galvanic isolation. In this work, the hardware architecture for bi-directional energy harvester rated 3.5 kW is developed with Isolated Full Bridge Boost Converter (IFBBC) as well as Dual Active Bridge (DAB) Converter configuration using modular power electronics hardware which is identical for both solar PV array and battery energy storage. In IFBBC converter, the current fed full bridge circuit is enabled and voltage fed full bridge circuit is disabled through Pulse Width Modulation (PWM) pulses for boost mode of operation and vice-versa for buck mode of operation. In DAB converter, all the switches are in active state so as to adjust the phase shift angle between primary full bridge and secondary full bridge which in turn decides the power flow directions depending on modes (boost/buck) of operation. Here, the control algorithm is developed to ensure the regulation of the common DC link voltage and maximum power extraction from the renewable energy sources depending on the selected mode (buck/boost) of operation. The circuit analysis and simulation study are conducted using PSIM 9.0 in three scenarios which are - 1.IFBBC with passive clamp, 2. IFBBC with active clamp, 3. DAB converter. In this work, a common hardware prototype for bi-directional energy harvester with 3.5 kW rating is built for IFBBC and DAB converter configurations. The power circuit is equipped with right choice of MOSFETs, gate drivers with galvanic isolation, high frequency transformer, filter capacitors, and filter boost inductor. The experiment was conducted for IFBBC converter with passive clamp under boost mode and the prototype confirmed the simulation results showing the measured efficiency as 88% at 2.5 kW output power. The digital controller hardware platform is developed using floating point microcontroller TMS320F2806x from Texas Instruments. The firmware governing the operation of the bi-directional energy harvester is written in C language and developed using code composer studio. The comprehensive analyses of the power circuit design, control strategy for battery charging/discharging under buck/boost modes and comparative performance evaluation using simulation and experimental results will be presented.

Keywords: bi-directional energy harvester, dual active bridge, isolated full bridge boost converter, intelligent green energy management system, maximum power point tracking, renewable energy sources

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Authors: Nicanor Garcia Alvarez, Belarmino Adenso-Diaz, Laura Calzada Infante

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In recent years, researchers have shown an interest in modelling maritime traffic as a complex network. In this paper, we propose a bipartite weighted network to model maritime traffic and detect port maritime communities. The bipartite weighted network considers two different types of nodes. The first one represents Spanish ports, while the second one represents the countries with which there is major import/export activity. The flow among both types of nodes is modeled by weighting the volume of product transported. To illustrate the model, the data is segmented by each type of traffic. This will allow fine tuning and the creation of communities for each type of traffic and therefore finding similar ports for a specific type of traffic, which will provide decision-makers with tools to search for alliances or identify their competitors. The traffic with the greatest impact on the Spanish gross domestic product is selected, and the evolution of the communities formed by the most important ports and their differences between 2019 and 2009 will be analyzed. Finally, the set of communities formed by the ports of the Spanish port system will be inspected to determine global similarities between them, analyzing the sum of the membership of the different ports in communities formed for each type of traffic in particular.

Keywords: bipartite networks, competition, infomap, maritime traffic, port communities

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Authors: Kruti Dave

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Social media marketing is an essential component of 21st-century business. Social media platforms enable small and medium-sized businesses to enhance brand recognition, generate leads and sales. However, the research on social media marketing is still fragmented and focuses on specific topics, such as effective communication techniques. Since the various ways in which social media impacts individuals and companies alike, the authors of this article focus on the origin, impacts, and current state of Social Media, emphasizing their significance as customer empowerment agents. It illustrates their potential and current responsibilities as part of the corporate business strategy and also suggests several methods to engage them as marketing tools. The focus of social media marketing ranges from defenders to explorers, the culture of Social media marketing encompasses the poles of conservatism and modernity, social media marketing frameworks lie between hierarchies and networks, and its management goes from autocracy to anarchy. This research proposes an integrative framework for small and medium-sized businesses through social media, and the influence of the same will be measured. This strategy will help industry experts to understand this new era. We propose an axiom: Social Media is always a function of marketing as a revenue generator.

Keywords: social media, marketing strategy, media marketing, brand awareness, customer engagement, revenue generator, brand recognition

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Authors: O. S. Ebrahim, K. O. Shawky, M. O. S. Ebrahim, P. K. Jain

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Induction Motor (IM) driving pump is the main consumer of electricity in a typical fluid transportation system (FTS). It was illustrated that changing the connection of the stator windings from delta to star at no load could achieve noticeable active and reactive energy savings. This paper proposes a supervisory hysteresis liquid-level control with three-state energy saving mode (ESM) for IM in FTS including storage tank. The IM pump drive comprises modified star/delta switch and hydromantic coupler. Three-state ESM is defined, along with the normal running, and named analog to computer ESMs as follows: Sleeping mode in which the motor runs at no load with delta stator connection, hibernate mode in which the motor runs at no load with a star connection, and motor shutdown is the third energy saver mode. A logic flow-chart is synthesized to select the motor state at no-load for best energetic cost reduction, considering the motor thermal capacity used. An artificial neural network (ANN) state estimator, based on the recurrent architecture, is constructed and learned in order to provide fault-tolerant capability for the supervisory controller. Sequential test of Wald is used for sensor fault detection. Theoretical analysis, preliminary experimental testing and, computer simulations are performed to show the effectiveness of the proposed control in terms of reliability, power quality and energy/coenergy cost reduction with the suggestion of power factor correction.

Keywords: ANN, ESM, IM, star/delta switch, supervisory control, FT, reliability, power quality

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Authors: Taras Agryzkov, Jose L. Oliver, Leandro Tortosa, Jose Vicent

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This paper aims to demonstrate how network models can be used to understand and to deal with some aspects of urban complexity. As it is well known, the Theory of Architecture and Urbanism has been using for decades’ intellectual tools based on the ‘sciences of complexity’ as a strategy to propose theoretical approaches about cities and about architecture. In this sense, it is possible to find a vast literature in which for instance network theory is used as an instrument to understand very diverse questions about cities: from their commercial activity to their heritage condition. The contribution of this research consists in adding one step of complexity to this process: instead of working with one single primal graph as it is usually done, we will show how new network models arise from the consideration of two different primal graphs interacting in two layers. When we model an urban network through a mathematical structure like a graph, the city is usually represented by a set of nodes and edges that reproduce its topology, with the data generated or extracted from the city embedded in it. All this information is normally displayed in a single layer. Here, we propose to separate the information in two layers so that we can evaluate the interaction between them. Besides, both layers may be composed of structures that do not have to coincide: from this bi-layer system, groups of interactions emerge, suggesting reflections and in consequence, possible actions.

Keywords: graphs, mathematics, networks, urban studies

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Authors: C. C. Liang

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MSN used to be the most popular application for communicating among social networks, but Facebook chat is now the most popular. Facebook and MSN have similar characteristics, including usefulness, ease-of-use, and a similar function, which is the exchanging of information with friends. Facebook outperforms MSN in both of these areas. However, the adoption of Facebook and abandonment of MSN have occurred for other reasons. Functions can be improved, but users’ willingness to use does not just depend on functionality. Flow status has been established to be crucial to users’ adoption of cyber applications and to affects users’ adoption of software applications. If users experience flow in using software application, they will enjoy using it frequently, and even change their preferred application from an old to this new one. However, no investigation has examined choice behavior related to switching from Facebook to MSN based on a consideration of flow experiences and functions. This investigation discusses the flow experiences and functions of social-networking applications. Flow experience is found to affect perceived ease of use and perceived usefulness; perceived ease of use influences information ex-change with friends, and perceived usefulness; information exchange influences perceived usefulness, but information exchange has no effect on flow experience.

Keywords: consumer behavior, social media, technology acceptance model, flow experience

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Authors: Irene M. Arora

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Hypothesis: Attention Deficit Hyperactivity Disorder is the result of an underdeveloped prefrontal cortex which is the primary cause for the signs and symptoms seen as defining features of ADHD. Methods: Through ‘PubMed’, ‘Wiley’ and ‘Google Scholar’ studies published between 2011-2018 were evaluated, determining if a dysfunctional prefrontal cortex caused the characteristic symptoms associated with ADHD. The search terms "prefrontal cortex", "Attention-Deficit/Hyperactivity Disorder", "cognitive control", "frontostriatal tract" among others, were used to maximize the assortment of relevant studies. Excluded papers were systematic reviews, meta-analyses and publications published before 2010 to ensure clinical relevance. Results: Nine publications were analyzed in this review, all of which were non-randomized matched control studies. Three studies found a decrease in the functional integrity of the frontostriatal tract fibers in conjunction with four studies finding impaired frontal cortex stimulation. Prefrontal dysfunction, specifically medial and orbitofrontal areas, displayed abnormal functionality of reward processing in ADHD patients when compared to their normal counterparts. A total of 807 subjects were studied in this review, yielding that a little over half (54%) presented with remission of symptoms in adulthood. Conclusion: While the prefrontal cortex shows the highest consistency of impaired activity and thinner volumes in patients with ADHD, this is a heterogenous disorder implicating its pathophysiology to the dysfunction of other neural structures as well. However, remission of ADHD symptomatology in adulthood was found to be attributable to increased prefrontal functional connectivity and integration, suggesting a key role for the prefrontal cortex in the development of ADHD.

Keywords: prefrontal cortex, ADHD, inattentive, impulsivity, reward processing

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Authors: Harshan Baskar, Anirudh S. Chakravarthy, Prateek Garg, Divyam Goel, Abhijith S. Raj, Kshitij Kumar, Lakshya, Ravichandra Parvatham, V. Sushant, Bijay Kumar Rout

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In this paper, we introduce a new computer vision task called nighttime dehaze-enhancement. This task aims to jointly perform dehazing and lightness enhancement. Our task fundamentally differs from nighttime dehazing – our goal is to jointly dehaze and enhance scenes, while nighttime dehazing aims to dehaze scenes under a nighttime setting. In order to facilitate further research on this task, we release a new benchmark dataset called Reside-β Night dataset, consisting of 4122 nighttime hazed images from 2061 scenes and 2061 ground truth images. Moreover, we also propose a new network called NDENet (Nighttime Dehaze-Enhancement Network), which jointly performs dehazing and low-light enhancement in an end-to-end manner. We evaluate our method on the proposed benchmark and achieve SSIM of 0.8962 and PSNR of 26.25. We also compare our network with other baseline networks on our benchmark to demonstrate the effectiveness of our approach. We believe that nighttime dehaze-enhancement is an essential task, particularly for autonomous navigation applications, and we hope that our work will open up new frontiers in research. Our dataset and code will be made publicly available upon acceptance of our paper.

Keywords: dehazing, image enhancement, nighttime, computer vision

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Nidhal Azawi

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Colonoscopy procedure is widely used in the world to detect an abnormality. Early diagnosis can help to heal many patients. Because of the unavoidable artifacts that exist in colon images, doctors cannot detect a colon surface precisely. The purpose of this work is to improve the visual quality of colonoscopy videos to provide better information for physicians by removing some artifacts. This work complements a series of work consisting of three previously published papers. In this paper, Optic flow is used for motion compensation, and then consecutive images are aligned/registered to integrate some information to create a new image that has or reveals more information than the original one. Colon images have been classified into informative and noninformative images by using a deep neural network. Then, two different strategies were used to treat informative and noninformative images. Informative images were treated by using Lucas Kanade (LK) with an adaptive temporal mean/median filter, whereas noninformative images are treated by using Lucas Kanade with a derivative of Gaussian (LKDOG) with adaptive temporal median images. A comparison result showed that this work achieved better results than that results in the state- of- the- art strategies for the same degraded colon images data set, which consists of 1000 images. The new proposed algorithm reduced the error alignment by about a factor of 0.3 with a 100% successfully image alignment ratio. In conclusion, this algorithm achieved better results than the state-of-the-art approaches in case of enhancing the informative images as shown in the results section; also, it succeeded to convert the non-informative images that have very few details/no details because of the blurriness/out of focus or because of the specular highlight dominate significant amount of an image to informative images.

Keywords: optic flow, colonoscopy, artifacts, spatial temporal filter

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: Dileep G., Akash Singh, Dalchand Ahirwar, Arkadeep Ghosh, Ashutosh Purohit, Gaurav Guleria, Kshatriya Om Prashant, Pushkar Patel, Saksham Kumar, Vanshaj Nathani, Vikas Dangi, Shubhajit Roy Chowdhury, Varun Dutt

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Transcranial direct current stimulation (tDCS) has shown promise as a novel approach to enhance cognitive performance and provide therapeutic benefits for various brain disorders. However, the exact underlying brain mechanisms are not fully understood. We conducted a study to examine the brain's functional changes when subjected to simultaneous tDCS and music (Indian classical raga). During the study, participants in the experimental group underwent a 20-minute session of tDCS at two mA while listening to music (raga) for a duration of seven days. In contrast, the control group received a sham stimulation for two minutes at two mA over the same seven-day period. The objective was to examine whether repetitive tDCS could lead to the formation of additional functional connections between the medial prefrontal cortex (the stimulated area) and the auditory cortex in comparison to a sham stimulation group. In this study, 26 participants (5 female) underwent pre- and post-intervention scans, where changes were compared after one week of either tDCS or sham stimulation in conjunction with music. The study revealed significant effects of tDCS on functional connectivity between the stimulated area and the auditory cortex. The combination of tDCS applied over the mPFC and music resulted in newly formed connections. Based on our findings, it can be inferred that applying anodal tDCS over the mPFC enhances functional connectivity between the stimulated area and the auditory cortex when compared to the effects observed with sham stimulation.

Keywords: fNIRS, tDCS, neuroplasticity, music

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Authors: Mengmeng Liu, J. David Frost

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Interstate highway system within the Atlanta Perimeter plays an important role in residents’ daily life. The serious influence of Atlanta I-85 Collapses implies that transportation system in the region lacks a cohesive and comprehensive transportation plan. Therefore, disaster resilience analysis of the transportation system is necessary. Resilience is the system’s capability to persist or to maintain transportation services when exposed to changes or shocks. This paper analyzed the resilience of the whole transportation system within the Perimeter and see how removing interstates within the Perimeter will affect the resilience of the transportation system. The data used in the paper are Atlanta transportation networks and LEHD Origin-Destination Employment Statistics data. First, we calculate the traffic flow on each road section based on LEHD data assuming each trip travel along the shortest travel time paths. Second, we calculate the measure of resilience, which is flow-based connectivity and centrality of the transportation network, and see how they will change if we remove each section of interstates from the current transportation system. Finally, we get the resilience function curve of the interstates and identify the most resilient interstates section. The resilience analysis results show that the framework of calculation resilience is effective and can provide some useful information for the transportation planning and sustainability analysis of the transportation infrastructures.

Keywords: connectivity, interstate highway system, network analysis, resilience analysis

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Authors: Jorge R. Santos, Pedro Sebastiao

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In recent years there has been a great growth in the use of drones, being used in several areas such as security, agriculture, or research. The existence of some systems that allow the remote control of drones is a reality; however, these systems are quite simple and directed to specific functionality. This paper proposes the development of a web platform made in Vue.js and Node.js to control, manage, and monitor drones in real time. Using a microservice architecture, the proposed project will be able to integrate algorithms that allow the optimization of processes. Communication with remote devices is suggested via HTTP through 3G, 4G, and 5G networks and can be done in real time or by scheduling routes. This paper addresses the case of forest fires as one of the services that could be included in a system similar to the one presented. The results obtained with the elaboration of this project were a success. The communication between the web platform and drones allowed its remote control and monitoring. The incorporation of the fire detection algorithm in the platform proved possible a real time analysis of the images captured by the drone without human intervention. The proposed system has proved to be an asset to the use of drones in fire detection. The architecture of the application developed allows other algorithms to be implemented, obtaining a more complex application with clear expansion.

Keywords: drone control, microservices, node.js, unmanned aerial vehicles, vue.js

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Authors: Anyia Nduka, Aslan Bin Amad Senin

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Taking a behavioral perspective of Organization Culture, Strategic Leadership, and performance (OC, SLP). We examine the role of Strategic Leadership as key vicious mechanism linking OC,SLP to the organizational capacities. Given the increasing degree of dependence of modern businesses on the use and scientific discovery of relevant data, research efforts around the entire globe have been accelerated. In today's corporate world, Strategic Leadership is still the most sustainable option of performance and competitive advantage. This is why it is critical to gain a deep understanding of research area and to strengthen new collaborative networks in efforts to support research transition towards these integrative efforts. This bibliometric analysis is aimed to examine global trends in OC,SLP research based on publication output, author co-authorships, and co-occurrences of author keywords among authors and affiliated countries. 2829 journal articles were retrieved from the Scopus database Between 1974 and 2021. From the research findings, there is a significant increase in number of publications with strong global collaboration (e.g., USA & UK). We also discovered that while most countries/territories without affiliations were centered in developing countries, the outstanding performance of Asian countries and the volume of their collaborations should be emulated.

Keywords: organizational culture, strategic leadership, organizational resilience, performance

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Authors: Alim Asamatdinov

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Superabsorbent polymer hydrogels can swell to absorb huge volumes of water or aqueous solutions. This property has led to many practical applications of these new materials, particularly in agriculture for improving the water retention of soils and the water supply of plants. This article reviews the methods of polymeric hydrogels, measurements and treatments of their properties, as well as their effects on soil and on plant growth. The thermodynamic approach used to describe the swelling behaviour of polymer networks proves to be quite helpful in modelling the hydrogel efficiency of water-absorbing additives. The paper presents the results of a study of the physical and chemical properties of hydrogels based on of the production of "Nitron" (Polyacrylonitrile) wastes fibre and salts of the 3-rd transition metals and formalin. The developed hydrogels HG-Al, HG-Cr and HG-formalin have been tested for water holding the capacity of sand. Such a conclusion was also confirmed by data from the method of determining the wilting point by vegetative thumbnails. In the entering process using a dose of 0.1% of the swelling polymeric hydrogel in sand with a culture of barley the difference between the wilting point in comparison with the control was negligible. This indicates that the moisture which was contained in the hydrogel is involved in moisture availability for plant growth, to the same extent as that in the capillaries.

Keywords: hydrogel, chemical, polymer, sandy, colloid

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Authors: N. Mehrotra, V. Ahuja, N. Sridharan

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Resilience is an all hazard and a proactive approach, require a multidisciplinary input in the inter related variables of the city system. This research based to identify and operationalize indicators for assessment in domain of institutions, infrastructure and knowledge, all three operating in task oriented community networks. This paper gives a brief account of the methodology developed for assessment of Urban Resilience and its prioritized components for a target population within a newly planned urban complex integrating Surajpur and Kasna village as nodes. People’s perception of Urban Resilience has been examined by conducting questionnaire survey among the target population of Greater Noida. As defined by experts, Urban Resilience of a place is considered to be both a product and process of operation to regain normalcy after an event of disturbance of certain level. Based on this methodology, six indicators are identified that contribute to perception of urban resilience both as in the process of evolution and as an outcome. The relative significance of 6 R’ has also been identified. The dependency factor of various resilience indicators have been explored in this paper, which helps in generating new perspective for future research in disaster management. Based on the stated factors this methodology can be applied to assess urban resilience requirements of a well planned town, which is not an end in itself, but calls for new beginnings.

Keywords: disaster, resilience, system, urban

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Authors: James Hilton Williams

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Recent tsunami events, including the 2011 Tohoku Tsunami, Japan, and the 2015 Illapel Tsunami, Chile, have highlighted the potential for tsunami impacts on the built environment. International research in the tsunami impacts domain has been largely focused toward impacts on buildings and casualty estimations, while only limited attention has been placed on the impacts on infrastructure which is critical for the recovery of impacted communities. New Zealand, with 75% of the population within 10 km of the coast, has a large amount of coastal infrastructure exposed to local, regional and distant tsunami sources. To effectively manage tsunami risk for New Zealand critical infrastructure, including energy, transportation, and communications, the vulnerability of infrastructure networks and components must first be determined. This research develops infrastructure asset vulnerability, functionality and repair- cost functions based on international post-event tsunami impact assessment data from technologically similar countries, including Japan and Chile, and adapts these to New Zealand. These functions are then utilized within a New Zealand based impact framework, allowing for cost benefit analyses, effective tsunami risk management strategies and mitigation options for exposed critical infrastructure to be determined, which can also be applied internationally.

Keywords: impact assessment, infrastructure, tsunami impacts, vulnerability functions

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Authors: Charity Ifunanya Osakwe, Victoria Ogochuchukwu Obi-Nwosu, Chima Kenneth Anachedo

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The study investigates how automated teller machines (ATM) and mobile banking affect deposit money banks in the Nigerian economy. The study made use of time series data which were obtained from the Central Bank of Nigeria Statistical Bulletin from 2009 to 2021. The Central Bank of Nigeria (CBN) data on automated teller machine and mobile phones were used to proxy electronic banking while total deposit in banks proxied the performance of deposit money banks. The analysis for the study was done using ordinary least square econometric technique with the aid of economic view statistical package. The results show that the automated teller machine has a positive and significant effect on the total deposits of deposit money banks in Nigeria and that making use of deposits of deposit money banks in Nigeria. It was concluded in the study that e-banking has equally increased banking access to customers and also created room for banks to expand their operations to more customers. The study recommends that banks in Nigeria should prioritize the expansion and maintenance of ATM networks as well as continue to invest in and develop more mobile banking services.

Keywords: electronic, banking, automated teller machines, mobile, deposit

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Authors: Peter J. Glinos

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The Alternative Education Resource Organization (AERO), one of the largest international hubs for alternative educators led by Jerry Mintz, has had a major impact on the global alternative education movement. The organization’s publications, like the AERO-Gramme Newsletter and its successor, the Education Revolution Magazine, allowed members across the globe to discuss issues, share support, and submit writings on policies and reforms. Stored on AERO's online digital archive, this work uses these publications from 1989 to 2011 to investigate the network's entanglements with America, Canada, Russia, Ukraine, Israel, Palestine, Japan, India, and Guatemala. Inspired by Reinhart Koselleck, this historical analysis will trace AERO’s entanglements within the United States, Japan, and Russia, contextualizing each of these multiple temporalities within the history of each nation’s education system, the developments within AERO, and the global geo-political climate at the time of AERO’s expansion. To help remedy the lack of attention paid by global historians to the role state organizations play supporting global networks, as noted in What is Global History? by Sebastian Conrad, this work will focus on the relationship between AERO and state actors.

Keywords: global history, history of education, neoliberalism, transnational history, alternative education

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Authors: Salha Abdullah Ali Al-Shamrani, Maha Muhammad Dhaher Aljuhani, Eman Ali Ahmed Aldhaheri

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With the proliferation of Internet of Things (IoT) devices in various industries, there has been a concurrent rise in security vulnerabilities, particularly spoofing attacks. This study explores the potential of blockchain technology in enhancing the security of IoT systems and mitigating these attacks. Blockchain's decentralized and immutable ledger offers significant promise for improving data integrity, transaction transparency, and tamper-proofing. This research develops and implements a blockchain-based IoT architecture and a reference network to simulate real-world scenarios and evaluate a blockchain-integrated intrusion detection system. Performance measures including time delay, security, and resource utilization are used to assess the system's effectiveness, comparing it to conventional IoT networks without blockchain. The results provide valuable insights into the practicality and efficacy of employing blockchain as a security mechanism, shedding light on the trade-offs between speed and security in blockchain deployment for IoT. The study concludes that despite minor increases in time consumption, the security benefits of incorporating blockchain technology into IoT systems outweigh potential drawbacks, demonstrating a significant potential for blockchain in bolstering IoT security.

Keywords: internet of things, spoofing, IoT, access control, blockchain, raspberry pi

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Authors: Ali Kadivar, Kaveh Niayesh

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MVDC markets for green power generations, Navy, subsea oil and gas electrification, and transportation electrification are extending rapidly. The lack of fast and powerful DC circuit breakers (CB) is the most significant barrier to realizing the medium voltage DC (MVDC) networks. A concept of hybrid circuit breakers (HCBs) benefiting from ultrafast disconnectors (UFD) is proposed. A set of mechanical switches substitute the power electronic commutation switches to reduce the losses during normal operation in HCB. The success of current commutation in such breakers relies on the behaviour of elongated, wall constricted arcs during the opening across the contacts inside the UFD. The arc voltage dependencies on the contact speed of UFDs is discussed through multiphysics simulations contact opening speeds of 10, 20 and 40 m/s. The arc voltage at a given current increases exponentially with the contact opening velocity. An empirical equation for the dynamic arc characteristics is presented for the tested UFD, and the experimentally verfied characteristics for voltage-current are utilized for the current commutation simulation prior to apply on a 14 kV experimental setup. Different failures scenarios due to the current commutation are investigated

Keywords: MVDC breakers, DC circuit breaker, fast operating breaker, ultra-fast elongated arc

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Authors: Sojung Kim

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This paper explores how the Partition of Korea is absorbed into everyday life through North Korean migrant women’s rituals for traditional holidays in Korea. In national holidays called myungjul, Koreans traditionally visit their paternal ancestor’s hometowns to hold jesa, the rites for the ancestors, at the graves and home. Due to the physical gaps in the kinship networks, marked by the kin left behind in North Korea, North Korean migrants gather among themselves in the neighborhood in South Korea as if they make the myungjul ritual of the family gatherings. This impossibility of the proper practice of the rites insinuates the violence of the Partition refracted into the family relations between those in the South and those in the North. Yet, the myungjul gathering creates a kind of collective hometown, beside one’s genealogical hometown, where they can express lamentation and guilt over not being able to visit their parents and ancestors in their hometowns, which they are traditionally required to do. In this as-if ritual, myungjul is re-created for and by the women and for others in the community. Yet, the texture of this ritual is marked by discontent and dissatisfaction. Attending to fostering discontents that seep into the collective events, this paper aims to seek ways to study the violence that permeated in everyday life in partitioned Korea.

Keywords: as-if ritual, everyday life, kinship, migration

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Authors: R. N. Begum, Ambalika Sharma, G. K. Singh

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Identity theft has serious ramifications beyond data and personal information loss. This necessitates the implementation of robust and efficient user identification systems. Therefore, automatic biometric recognition systems are the need of the hour, and ECG-based systems are unquestionably the best choice due to their appealing inherent characteristics. The CNNs are the recent state-of-the-art techniques for ECG-based user identification systems. However, the results obtained are significantly below standards, and the situation worsens as the number of users and types of heartbeats in the dataset grows. As a result, this study proposes a highly accurate and resilient ECG-based person identification system using CNN's dense learning framework. The proposed research explores explicitly the calibre of dense CNNs in the field of ECG-based human recognition. The study tests four different configurations of dense CNN which are trained on a dataset of recordings collected from eight popular ECG databases. With the highest FAR of 0.04 percent and the highest FRR of 5%, the best performing network achieved an identification accuracy of 99.94 percent. The best network is also tested with various train/test split ratios. The findings show that DenseNets are not only extremely reliable but also highly efficient. Thus, they might also be implemented in real-time ECG-based human recognition systems.

Keywords: Biometrics, Dense Networks, Identification Rate, Train/Test split ratio

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Authors: Olaiya Folorunsho, Comfort Y. Daramola, Joel N. Ugwu, Lawrence B. Adewole, Olufisayo S. Ekundayo

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In today world, there is a tremendous rise in the usage of internet due to the fact that almost all the communication and information sharing is done over the web. Conversely, there is a continuous growth of unauthorized access to confidential data. This has posed a challenge to information security expertise whose major goal is to curtail the menace. One of the approaches to secure the safety delivery of data/information to the rightful destination without any modification is steganography. Steganography is the art of hiding information inside an embedded information. This research paper aimed at designing a secured algorithm with the use of image steganographic technique that makes use of Least Significant Bit (LSB) algorithm for embedding the data into the bit map image (bmp) in order to enhance security and reliability. In the LSB approach, the basic idea is to replace the LSB of the pixels of the cover image with the Bits of the messages to be hidden without destroying the property of the cover image significantly. The system was implemented using C# programming language of Microsoft.NET framework. The performance evaluation of the proposed system was experimented by conducting a benchmarking test for analyzing the parameters like Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The result showed that image steganography performed considerably in securing data hiding and information transmission over the networks.

Keywords: steganography, image steganography, least significant bits, bit map image

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Authors: William Cheng, Rini Sekartini

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Background: Sleep is essential for children because it provides enhancement for the neural system activities that give physiologic effects for the body to support growth and development. One of the modifiable factors that relates with sleep is nutrition, which includes nutritional status, iron intake, and magnesium intake. Nutritional status represents the balance between nutritional intake and expenditure, while iron and magnesium are micronutrients that are related to sleep regulation. The aim of this study is to identify prevalence of sleep disturbance among Indonesian children and to evaluate its relation with aspect to nutrition. Methods : A cross-sectional study involving children aged 5 to 7-years-old in an urban primary health care between 2012 and 2013 was carried out. Related data includes anthropometric status, iron intake, and magnesium intake. Iron and magnesium intake was obtained by 24-hours food recall procedure. Sleep Disturbance Scale for Children (SDSC) was used as the diagnostic tool for sleep disturbance, with score under 39 indicating presence of problem. Results: Out of 128 school-aged children included in this study, 28 (23,1%) of them were found to have sleep disturbance. The majority of children had good nutritional status, with only 15,7% that were severely underweight or underweight, and 12,4% that were identified as stunted. On the contrary, 99 children (81,8%) were identified to have inadequate magnesium intake and 56 children (46,3%) with inadequate iron intake. Our analysis showed there was no significant relation between all of the nutritional status indicators and sleep disturbance (p>0,05%). Moreover, inadequate iron and magnesium intake also failed to prove significant relation with sleep disturbance in this population. Conclusion: Almost fourth of school-aged children in Indonesia were found to have sleep disturbance and further study are needed to overcome this problem. According to our finding, there is no correlation between nutritional status, iron intake, magnesium intake, and sleep disturbance.

Keywords: iron intake, magnesium intake, nutritional status, school-aged children, sleep disturbance

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Authors: Hamzeh Alipour, Masoumeh Bagheri, Tahereh Karamzadeh, Abbasali Raz, Kourosh Azizi

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Netrin-A, a protein identified for conducting commissural axons, has a similar role in angiogenesis. In addition, studies have shown that one of the netrin-A receptors is expressed in the growing cells of small capillaries. It will be interesting to study this new group of molecules because their role in wound healing will become clearer in the future due to angiogenesis. The greenbottle blowfly Luciliasericata (L. sericata) larvae are increasingly used in maggot therapy of chronic wounds. This aim of this was the identification of moleculareatures of Netrin-A in L. sericata larvae. Larvae were reared under standard maggotarium conditions. The nucleic acid sequence of L. sericataNetrin-A (LSN-A) was then identified using Rapid Amplification of cDNA Ends (RACE) and Rapid Amplification of Genomic Ends (RAGE). Parts of the Netrin-A gene, including the middle, 3′-, and 5′-ends were identified, TA cloned in pTG19 plasmid, and transferred into DH5ɑ Escherichia coli. Each part was sequenced and assembled using SeqMan software. This gene structure was further subjected to in silico analysis. The DNA of LSN-A was identified to be 2407 bp, while its mRNA sequence was recognized as 2115 bp by Oligo0.7 software. It translated the Netrin-A protein with 704 amino acid residues. Its molecular weight is estimated to be 78.6 kDa. The 3-D structure ofNetrin-A drawn by SWISS-MODEL revealed its similarity to the Netrin-1 of humans with 66.8% identity. The LSN-A protein conduces to repair the myelin membrane in neuronal cells. Ultimately, it can be an effective candidate in neural regeneration and wound healing. Furthermore, our next attempt is to deplore recombinant proteins for use in medical sciences.

Keywords: maggot therapy, netrin-A, RACE, RAGE, lucilia sericata

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Authors: Georgios Linardatos, Miltiadis Zamparas, Vlasoula Bekiari, Georgios Bokias, Georgios Hotos

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Hydrogels are three-dimensional, hydrophilic, polymeric networks composed of homopolymers or copolymers and are insoluble in water due to the presence of chemical or physical cross-links. When hydrogels come in contact with aqueous solutions, they can effectively sorb and retain the dissolved substances, depending on the nature of the monomeric units comprising the hydrogel. For this reason, hydrogels have been proposed in several studies as water purification agents. At the present work anionic hydrogels bearing negatively charged –COO- groups were prepared and investigated. These gels are based on sodium acrylate (ANa), either homopolymerized (poly(sodiumacrylate), PANa) or copolymerized (P(DMAM-co-ANa)) with N,N Dimethylacrylamide (DMAM). The hydrogels were used to extract some model organic dyes from water. It is found that cationic dyes are strongly sorbed and retained by the hydrogels, while sorption of anionic dyes was negligible. In all cases it was found that both maximum sorption capacity and equilibrium binding constant varied from one dye to the other depending on the chemical structure of the dye, the presence of functional chemical groups and the hydrophobic-hydrophilic balance. Finally, the nonionic hydrogel of the homopolymer poly(N,N-dimethylacrylamide), PDMAM, was also used for reasons of comparison.

Keywords: anionic organic hydrogels, sorption, organic dyes, water purification agents

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Authors: Yousef Almutairi, Yajue Wu

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Various large-scale trends have characterized the current century thus far, including increasing shifts towards urbanization and greater movement. It is predicted that there will be 9.3 billion people on Earth in 2050 and that over two-thirds of this population will be city dwellers. Moreover, in larger cities worldwide, mass transportation systems, including underground systems, have grown to account for the majority of travel in those settings. Underground networks are vulnerable to fires, however, endangering travellers’ safety, with various examples of fire outbreaks in this setting. This study aims to increase knowledge of the impacts of extreme climatic conditions on fires, including the role of the high ambient temperatures experienced in Middle Eastern countries and specifically in Saudi Arabia. This is an element that is not always included when assessments of fire safety are made (considering visibility, temperatures, and flows of smoke). This paper focuses on a tunnel within Riyadh’s underground system as a case study and includes simulations based on computational fluid dynamics using ANSYS Fluent, which investigates the impact of various ventilation systems while identifying smoke density, speed, pressure and temperatures within this tunnel.

Keywords: fire, subway tunnel, CFD, mechanical ventilation, smoke, temperature, harsh weather

Procedia PDF Downloads 117